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This paper conducts a scaling study for fMRI foundation models, revealing that performance depends on the combination of pretraining data size, model size, and training duration, not just compute.
MnemoDyn is a dynamical-systems based model trained on 40K fMRI sequences for learning resting-state dynamics, outperforming transformer-based approaches in reconstruction quality and generalizing well across diverse populations.
This paper presents a zero-shot time-series foundation model applied to prediction and causal analysis of functional MRI and synthetic signals.
This paper tracks how supervised training with different learning rules (backpropagation, feedback alignment, predictive coding, STDP) degrades alignment between neural network representations and early visual cortex fMRI data, finding that untrained networks often match or exceed trained ones in V1 alignment.
This paper proposes DSFM, a novel generative framework that uses wavelet decomposition and spectral flow matching to synthesize realistic fMRI time series for brain disorder identification, addressing data scarcity and non-stationarity challenges.
Investigates neural integral-operator-based models for fMRI encoding and decoding tasks, focusing on the role of nonlocal spatiotemporal context and showing that larger temporal windows improve performance across datasets.